Add 2D Autoencoder and GMM Integration on Siracusa Target by Aldrago98 · Pull Request #190 · pulp-platform/Deeploy

Aldrago98 · 2026-05-06T15:17:15Z

The goal of this branch is to implement a 2D autoencoder model integrated with a Gaussian Mixture Model (GMM) on the Siracusa target with Neureka support.

The implementation was developed incrementally through the following steps:

initial support for a generic target;
porting to Siracusa without tiling;
introduction of tiled support on Siracusa;
final integration with Siracusa + Neureka.

Added

2D Autoencoder and GMM models (Models/Autoencoder2D/{STD,GMM}, Models/GMM) plus Autoencoder2D micro-block tests (Models/Autoencoder2D/MicroBlocks/*) for Generic and Siracusa (untiled, L2 tiled and L3 tiled)
FP32 ConvTranspose2D (with padding, dilation and groups) for Generic (ConvTranspose2DParser, ConvTranspose2d_fp32) and for PULPOpen/Siracusa with a parallel kernel (ConvTranspose_fp32.c) and ConvTranspose2DTileConstraint
FP32 ReduceLogSumExp operator for Generic and PULPOpen (parser, layer, type checker, template and kernel), supporting attribute- and input-provided axes, negative axes and keepdims
Tiled Pad on PULPOpen via a new Pad2DTileConstraint (Pad1D uses UntiledTileConstraint), including support for the opset-11+ Pad form with constant pads/value inputs
FP32 BatchNormalization on PULPOpen, and channels-last layout plus non-default epsilon support in the Generic FP32 BatchNorm kernel
Per-node engine selection: a node with an engine attribute is mapped to that engine in NetworkContainer._selectEngine
Tensor-by-tensor broadcasting in FP32 Mul on PULPOpen (non-scalar second operand)
Kernel tests Kernels/FP32/BatchNorm/{ChannelsFirst,ChannelsLast,NonDefaultEpsilon} and Kernels/FP32/Conv/Transpose_2D, and unit tests test_mul_layer.py and test_reduce_log_sum_exp_parser.py
Siracusa L3-tiled kernel tests Kernels/FP32/Slice/{StartStep2,NegativeStep} (input larger than L2, non-zero starts on tiled axes, strided and reversed slices)
Siracusa tiled Pad2D kernel tests Kernels/FP32/Pad2D/PaddingTail and Kernels/Integer/Pad/PaddingTail_2D (L1 budgets that leave a tail tile made only of bottom padding) and the L3-tiled Kernels/FP32/Pad2D/L3

Changed

Split the Generic/X-HEEP ConvTranspose mapping into ConvTranspose1DMapper and ConvTranspose2DMapper (BasicConvTransposeBindings renamed to BasicConvTranspose1DBindings)
UntiledTileConstraint now emits a full-tensor load schedule, so untiled operators can still be moved across memory levels (e.g. L3 -> L2 -> L1)
BatchNorm and RequantShift parsing infer channels-first vs. channels-last from the parameter length, for graphs that mix layouts
MulLayer computes proper NumPy-style broadcast shapes
Refactor N-EUREKA convolution checks in NeurekaEngine to share common preconditions and tolerate missing optional attributes
ONNXLayer._broadcastToNpType keeps the float width (e.g. float32) instead of casting constants to double
Float constants are emitted as valid C float literals with full float32 precision
The test runner passes --cores to the generator for tiled runs
Siracusa/PULPOpen float output comparison uses a combined absolute + relative tolerance
Add pre-commit to requirements-dev.txt and run the reuse hook serially

Fixed

Reject non-finite outputs in the PULPOpen/Siracusa floating-point test comparison.
Preserve dynamic biases in MatMul/Add fusion and avoid fusing shapes or shared outputs incompatible with Gemm.
Restore PULPOpen floating-point Slice tiling with per-tile dimensions and local indices, including strided and reversed slices.
PULPOpen Slice tiling no longer applies the slice start twice with nested (L3 -> L2 -> L1) tiling: deeper levels compute the input tile relative to the parent input tile. The PULP Slice kernel now runs on all cluster cores, uses 32-bit indices and copies contiguous rows with memcpy.
PULPOpen Pad2D tiling: a tail tile made only of bottom padding no longer loads the row past the end of the input; the tile-local padding is computed from absolute offsets, so nested (L3 -> L2 -> L1) tiling places the padding correctly; the input tile height is constrained to min(output tile height, input height) so every tile's input rows fit the L1 buffer; and the input sizes are taken from the tensor shape, so serializing again after the per-tile variable replacement no longer fails.
Align PULPOpen FP32 MaxPool kernel and stride arguments with the parser's height/width order, and correct directional padding for MaxPool2D.
Tiling codegen: when several operands reference the same external tensor (e.g. Mul(x, x)), transfer each tile and advance the external reference only once (single and double buffering)
Generic: run RemoveGlobalOutputReshapePass so a Reshape producing a graph output no longer leaves the global output buffer unwritten
Use uint16_t instead of int8_t for the MatMul tile replacement variables (M, N, O, batch) so tile sizes above 127 do not overflow
Cast the external address to uint32_t in the PULPOpen L3 DMA 2D transfer template

PR Merge Checklist

The PR is rebased on the latest devel commit and pointing to devel.
Your PR reviewed and approved.
All checks are passing.
The CHANGELOG.md file has been updated.
If the docker was modified, change back its link after review.

diaconuccalin

Submitting the first third part of the review, I will finish going through the rest of the files as soon as possible.

diaconuccalin

Second part of my review. Working on the third and hopefully final part :)

diaconuccalin

Review finished. Other than the comments I left across the files, it would be useful to have some more testing for all the modified elements. Namely:

batchnorm with and without epsilon, with and without channel first
larger ConvTranspose2D with tighter memory limits, to force tiling
multiplication where B is not scalar, but has the same size as A
ReduceLogSumExp with one and more reduction axis
Pad and Slice, with thight mem limits for tiling
Full 2D autoencoder and GMM models, with tighter mem limits for tiling
MaxPool on non-square input, since you inverted the width and hight

FrancescoConti · 2026-07-30T12:38:55Z

@diaconuccalin if @Aldrago98 can not complete this due to other activities side-tracking, maybe you can take over (I guess that @marchioa can also help for what concerns any changes to NEureka)

diaconuccalin

Mostly small changes needed. I will pull out of draft. Remember to rebase to most recent devel, and update the changelog file also. Also, there is a conflict that needs to be solved before the tests can run.

coderabbitai · 2026-09-22T14:15:09Z

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Summary

Summary by CodeRabbit

New Features
- Added support for ReduceLogSumExp operations, including FP32 execution.
- Added 2D transposed convolution support for Generic and PULPOpen targets.
- Expanded padding, batch normalization, and floating-point concatenation support.
- Added improved PULPOpen tiling for padding, batch normalization, and transposed convolution.
Bug Fixes
- Corrected NumPy-style broadcasting for multiplication.
- Improved handling of ONNX padding inputs and optional batch-normalization settings.
- Improved floating-point output comparisons using absolute and relative tolerances.
Tests
- Added coverage for new operators, broadcasting behavior, and FP32 kernels.

The change adds generic and PULPOpen support for ReduceLogSumExp and 2D ConvTranspose. It updates padding, batch normalization, broadcasting, tiling, kernels, engine checks, floating-point validation, tests, and development configuration.

Changes: Operator support and target integration

Layer / File(s)	Summary
Core graph and code-generation updates `Deeploy/DeeployTypes.py`, `Deeploy/Targets/Generic/TopologyOptimizationPasses/Passes.py`, `Deeploy/Targets/Neureka/Engine.py`, `Deeploy/TilingExtension/...`	Constant handling, NumPy type mapping, engine selection, pointer initialization, MatMul/Add merging, convolution validation, and reference hoisting are updated.
Generic operator parsing and execution `Deeploy/Targets/Generic/...`, `TargetLibraries/Generic/...`	ReduceLogSumExp and 2D ConvTranspose parsing, mapping, templates, bindings, headers, and kernels are added. Mul broadcasting, Pad, BatchNorm, ConvTranspose strides, Relu, Concat, and float multiplication are updated.
PULPOpen integration and tiling `Deeploy/Targets/PULPOpen/...`, `TargetLibraries/PULPOpen/...`	PULPOpen mappings, bindings, parsers, kernels, DMA handling, padding constraints, ConvTranspose constraints, and tiling-ready bindings are added or updated.
Validation and development configuration `DeeployTest/...`, `.pre-commit-config.yaml`, `requirements-dev.txt`, `.vscode/launch.json`	Floating-point comparisons use absolute and relative tolerances. Tests cover broadcasting, ReduceLogSumExp, BatchNorm, and 2D ConvTranspose. Tiled test generation passes the core count. Development dependencies are updated.

Estimated code review effort: 4 (Complex) | ~60 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant ONNXGraph
  participant PULPParser
  participant PULPMapping
  participant Tiler
  participant PULPKernel
  ONNXGraph->>PULPParser: parse ConvTranspose or ReduceLogSumExp
  PULPParser->>PULPMapping: create mapped operator
  PULPMapping->>Tiler: apply tiling-ready binding
  Tiler->>PULPKernel: generate tiled kernel call

Suggested reviewers: runwangdl

Merge Risk: High · previous review ba191

Common operator configurations can produce wrong results or fail code generation, so the remaining correctness and compilation issues should be fixed before merging.

Pre-merge checks: 4

Check name	Status	Explanation
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.
Title check		The title clearly identifies the primary objective: integrating a 2D autoencoder and GMM on the Siracusa target. The changes add supporting 2D ConvTranspose, BatchNorm, tiling, Neureka, and related in…
Description check		The pull request description is directly related to the changeset. It describes the 2D autoencoder and GMM integration, ConvTranspose2D, ReduceLogSumExp, BatchNorm, padding, tiling, Neureka support, t…

Full details: Title check

Explanation

The title clearly identifies the primary objective: integrating a 2D autoencoder and GMM on the Siracusa target. The changes add supporting 2D ConvTranspose, BatchNorm, tiling, Neureka, and related integration work.

Full details: Description check

Explanation

The pull request description is directly related to the changeset. It describes the 2D autoencoder and GMM integration, ConvTranspose2D, ReduceLogSumExp, BatchNorm, padding, tiling, Neureka support, test updates, and related fixes present in the pull request.

Finishing Touches

Resolve merge conflicts

Resolve merge conflict in branch FIORIRE2

🧪 Generate unit tests (beta)

Create a new PR

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Actionable comments posted: 10

🪄 Fix CodeRabbit comments on this PR

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Inline comments:
In `@Deeploy/DeeployTypes.py`:
- Around line 2872-2881: Update the NULL-initialization condition around
VariableBuffer handling to exclude _ReferenceBuffer instances before applying
the pointer-type checks. Preserve the existing behavior for other VariableBuffer
pointers and continue using node.init() for excluded reference buffers,
preventing duplicate declarations with _ReferenceBuffer.allocTemplate.

In `@Deeploy/Targets/Generic/Layers.py`:
- Around line 449-462: Update computeShapes to process every axis in
operatorRepresentation['axes'], not only the first. Preserve dimensions as 1 for
all axes when keepdims is enabled; otherwise remove all specified axes while
retaining the scalar fallback [1].
- Around line 345-352: Update MulLayer.computeShapes to keep the original
rhsShape for singleton right-hand operands, identified by np.prod(rhsShape) ==
1, while expanding all non-singleton broadcastable operands to broadcastShape.
Preserve the existing broadcastShape assignment for the left input and return
the updated shapes so scalar Mul operands remain singleton.

In `@Deeploy/Targets/Generic/Templates/ConvTransposeTemplate.py`:
- Line 9: Update both bias pointer assignments in the ConvTranspose templates to
retrieve bias through the Mako context, using context.get('bias') with the
existing NULL fallback so missing bias remains supported under strict_undefined.

In `@Deeploy/Targets/Generic/TileConstraints/UntiledTileConstraint.py`:
- Around line 80-87: Update the constraint filter in extractBaseAddr to consider
only inputTensorMemoryConstraints and outputTensorMemoryConstraints, excluding
values found solely in intermediateTensorMemoryConstraints while preserving the
existing buffer checks and address-name collection.

In `@Deeploy/Targets/PULPOpen/TileConstraints/PadTileConstraint.py`:
- Line 19: Bind the Pad2D tiling configuration to Pad2DTileConstraint instead of
UntiledTileConstraint so Pad2DMapper uses the new tiled behavior. Update the
PULPPad2DTilingReadyBindings definition and its imports, preserving the existing
PULPPad2DBindings and node bindings.

In `@Deeploy/Targets/PULPOpen/Tiler.py`:
- Around line 166-167: Update PULPSliceTilingReadyBindings to use
SliceTileConstraint() instead of UntiledTileConstraint(), and add the
corresponding SliceTileConstraint import so SliceMapper can perform Slice
tiling.

In `@TargetLibraries/Generic/src/ConvTranspose2d_fp32.c`:
- Around line 7-12: Update ConvTranspose2DParser.parseNode to reject non-zero
padding, dilation values other than [1, 1], and group values other than 1 before
emitting ConvTranspose2d_fp32 calls. Keep supported configurations unchanged and
ensure unsupported attributes fail explicitly rather than reaching the kernel,
which does not implement them.

In `@TargetLibraries/PULPOpen/inc/kernel/MaxPool.h`:
- Line 13: Update the argument order in the template invoking
PULP_MaxPool2d_fp32_fp32_HWC so dim_im_in_y (height) is passed before
dim_im_in_x (width), matching the kernel’s H, W, C signature and preserving
correct results for non-square inputs.

In `@TargetLibraries/PULPOpen/src/ConvTranspose_fp32.c`:
- Line 72: Restrict the loop-counter volatile qualifier in the K_w iteration to
LLVM builds by guarding it with __TOOLCHAIN_LLVM__; GCC builds must use a normal
uint32_t kw counter while preserving the existing loop bounds and increment.

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📥 Commits

Reviewing files that changed from the base of the PR and between aa26825 and ba191f9.

📒 Files selected for processing (89)

.pre-commit-config.yaml
.vscode/launch.json
Deeploy/DeeployTypes.py
Deeploy/Targets/Generic/Bindings.py
Deeploy/Targets/Generic/Layers.py
Deeploy/Targets/Generic/Parsers.py
Deeploy/Targets/Generic/Platform.py
Deeploy/Targets/Generic/Templates/BatchNormalizationTemplate.py
Deeploy/Targets/Generic/Templates/ConvTransposeTemplate.py
Deeploy/Targets/Generic/Templates/FloatMulTemplate.py
Deeploy/Targets/Generic/Templates/FloatReduceLogSumExpTemplate.py
Deeploy/Targets/Generic/TileConstraints/UntiledTileConstraint.py
Deeploy/Targets/Generic/TopologyOptimizationPasses/Passes.py
Deeploy/Targets/Generic/TypeCheckers.py
Deeploy/Targets/Neureka/Engine.py
Deeploy/Targets/PULPOpen/Bindings.py
Deeploy/Targets/PULPOpen/DMA/L3Dma.py
Deeploy/Targets/PULPOpen/Parsers.py
Deeploy/Targets/PULPOpen/Platform.py
Deeploy/Targets/PULPOpen/Templates/ConvTransposeTemplate.py
Deeploy/Targets/PULPOpen/Templates/FloatGemmTemplate.py
Deeploy/Targets/PULPOpen/Templates/FloatMulTemplate.py
Deeploy/Targets/PULPOpen/TileConstraints/ConvTransposeTileConstraint.py
Deeploy/Targets/PULPOpen/TileConstraints/MatMulTileConstraint.py
Deeploy/Targets/PULPOpen/TileConstraints/PadTileConstraint.py
Deeploy/Targets/PULPOpen/TileConstraints/SliceConstraint.py
Deeploy/Targets/PULPOpen/Tiler.py
Deeploy/TilingExtension/CodeTransformationPasses/TilingHoistingMixIn.py
DeeployTest/Platforms/Generic/main.c
DeeployTest/Platforms/PULPOpen/src/deeploytest.c
DeeployTest/Platforms/Siracusa/src/deeploytest.c
DeeployTest/Tests/Kernels/FP32/BatchNorm/ChannelsFirst/inputs.npz
DeeployTest/Tests/Kernels/FP32/BatchNorm/ChannelsFirst/network.onnx
DeeployTest/Tests/Kernels/FP32/BatchNorm/ChannelsFirst/outputs.npz
DeeployTest/Tests/Kernels/FP32/BatchNorm/ChannelsLast/inputs.npz
DeeployTest/Tests/Kernels/FP32/BatchNorm/ChannelsLast/network.onnx
DeeployTest/Tests/Kernels/FP32/BatchNorm/ChannelsLast/outputs.npz
DeeployTest/Tests/Kernels/FP32/BatchNorm/NonDefaultEpsilon/inputs.npz
DeeployTest/Tests/Kernels/FP32/BatchNorm/NonDefaultEpsilon/network.onnx
DeeployTest/Tests/Kernels/FP32/BatchNorm/NonDefaultEpsilon/outputs.npz
DeeployTest/Tests/Kernels/FP32/Conv/Transpose_2D/inputs.npz
DeeployTest/Tests/Kernels/FP32/Conv/Transpose_2D/network.onnx
DeeployTest/Tests/Kernels/FP32/Conv/Transpose_2D/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/GMM/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/GMM/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/GMM/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMR/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMR/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMR/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLRes/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLRes/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLRes/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLTra/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLTra/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLTra/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLin/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLin/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EncoderPCMRLin/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/Encoder_mini/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/Encoder_mini/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/Encoder_mini/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EnoderCMR/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EnoderCMR/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/MicroBlocks/EnoderCMR/outputs.npz
DeeployTest/Tests/Models/Autoencoder2D/STD/inputs.npz
DeeployTest/Tests/Models/Autoencoder2D/STD/network.onnx
DeeployTest/Tests/Models/Autoencoder2D/STD/outputs.npz
DeeployTest/Tests/Models/GMM/inputs.npz
DeeployTest/Tests/Models/GMM/network.onnx
DeeployTest/Tests/Models/GMM/outputs.npz
DeeployTest/testUtils/deeployRunner.py
DeeployTest/test_generic_config.py
DeeployTest/test_mul_layer.py
DeeployTest/test_reduce_log_sum_exp_parser.py
DeeployTest/test_siracusa_config.py
DeeployTest/test_siracusa_tiled_config.py
TargetLibraries/Generic/inc/DeeployBasicMath.h
TargetLibraries/Generic/inc/kernel/BatchNorm.h
TargetLibraries/Generic/inc/kernel/ConvTranspose2d_fp32.h
TargetLibraries/Generic/inc/kernel/ReduceLogSumExp.h
TargetLibraries/Generic/src/BatchNorm_fp32.c
TargetLibraries/Generic/src/ConvTranspose2d_fp32.c
TargetLibraries/Generic/src/ReduceLogSumExp_fp32.c
TargetLibraries/PULPOpen/inc/DeeployPULPMath.h
TargetLibraries/PULPOpen/inc/kernel/Conv.h
TargetLibraries/PULPOpen/inc/kernel/MaxPool.h
TargetLibraries/PULPOpen/src/ConvTranspose_fp32.c
TargetLibraries/PULPOpen/src/MaxPool.c
requirements-dev.txt

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diaconuccalin

Very few small issues remaining. I can also apply them if it's ok with you @Aldrago98

…xed erronous binder type for Pad

The NULL initializer in the PULP L1/L2 init templates only silenced compiler warnings, but it forced the I/O header to strip initializers from init() output, which in turn required declare()/declarationTemplate and MemPool/SoftHier overrides. Restore the devel behaviour for all of these. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

- Generic: run RemoveGlobalOutputReshapePass, as PULP, Snitch and Neureka already do. A Reshape producing a graph output was emitted as a pointer swap on the global output, so the testbench read an unwritten buffer (EncoderPCMRLRes returned all zeros). - Tiling codegen: when several operands reference the same external tensor (e.g. Mul(x, x)), transfer each tile and advance the external reference only once. Previously the reference was advanced once per operand, so the second operand overwrote the shared L1 tile with the wrong slice and later tiles read out of bounds (Models/GMM failed at L1=128). Fixed for both single and double buffering. - Add Models/GMM to the Siracusa L2 double-buffer tests to cover the double-buffering path. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

- SliceTileConstraint: at the first tiling level the input tile is the global bounding box of the output tile (from its absolute offset). At deeper levels (L3 -> L2 -> L1) it is computed relative to the parent input tile, which already contains the slice start, so the start is no longer applied twice (the L1 input tile overran the L2 buffer). The level is detected from the position of the target level in data_in's memory path. - PULP Slice kernel: split the output elements across all cluster cores instead of a single core, use 32-bit indices, and copy innermost rows with memcpy when the innermost step is 1. - Correct the empty-output error message, which is also raised untiled. - Add Siracusa L3-tiled kernel tests Kernels/FP32/Slice/{StartStep2, NegativeStep} (input larger than L2, non-zero starts on tiled axes, strided and reversed slices), single and double buffering. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

- Pad2DTileConstraint: a tail tile made only of bottom padding loads an in-bounds dummy row (the last input row) instead of the row past the end of the input; the kernel copies zero rows and only fills the pad value. - Compute the tile-local padding and input rows from absolute output offsets. With nested tiling (L3 -> L2 -> L1) the input tile is expressed relative to the parent input tile, so padding is no longer inserted at the start of every L2 tile. - Constrain the input tile height to min(output tile height, input height), so the input rows of every tile fit the L1 buffer (the previous bound allowed buffers 2 * pad_y rows too small). - Take the input sizes from the tensor shape instead of the operator representation, which holds per-tile reference names when TilingVariableReplacementUpdate serializes again. - Add Siracusa tiled kernel tests Kernels/FP32/Pad2D/PaddingTail and Kernels/Integer/Pad/PaddingTail_2D (L1 budgets that leave a padding-only tail tile) and the L3-tiled Kernels/FP32/Pad2D/L3. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

diaconuccalin assigned Aldrago98 May 6, 2026

diaconuccalin added the Feature Addition of new features label May 6, 2026

Aldrago98 changed the title ~~FIORIRE2~~ Add 2D Autoencoder and GMM Integration on Siracusa Target May 6, 2026

diaconuccalin requested changes May 20, 2026

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diaconuccalin requested changes May 22, 2026

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Aldrago98 force-pushed the FIORIRE2 branch from 29c57b2 to e2da705 Compare June 16, 2026 12:00

Aldrago98 force-pushed the FIORIRE2 branch from 1993f19 to 491ee95 Compare July 31, 2026 14:44

diaconuccalin requested changes Sep 22, 2026

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Comment thread DeeployTest/Platforms/Generic/main.c Resolved

Comment thread .vscode/launch.json Resolved

Comment thread Deeploy/DeeployTypes.py Resolved

Comment thread Deeploy/Targets/Generic/Layers.py Resolved

diaconuccalin marked this pull request as ready for review September 22, 2026 13:52

diaconuccalin requested review from Victor-Jung, Xeratec and runwangdl as code owners September 22, 2026 13:52

coderabbitai Bot reviewed Sep 22, 2026

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francesco.aldrigo.e and others added 14 commits October 20, 2026 13:52

Fix Debugger

3e6c32a

track files

8768f3f

ConvTranspose2D generic

23ef45c

fix batch norm dimension

aef02cf

model 2d

fbbab36

Fix Batch norm Dimension

ef73cb9

costant padding support

ab145b3

float notation fix

c1f10da

ConvTranspose2D

0040308

Memory Allocation

5f8e95d

fix Tiling

d469b51

Siracusa Workaround

2a95209

Warning Fix

4b42679

Format Folder

e72b6b1

Francesco Aldrigo added 6 commits October 21, 2026 11:54

Format code

8dc9d40

DMA Declaration Fix

6040382

Fix Format Bug and memory allocation

11c4421

Merge remote-tracking branch 'upstream/devel' into FIORIRE2

dfdafbd

Integration with xheep

ff429f6

Channel Order

448fe64

diaconuccalin mentioned this pull request Nov 4, 2026

[DRAFT] Slice operator tiling improvement #140

Closed

5 tasks

diaconuccalin linked an issue Nov 4, 2026 that may be closed by this pull request

[BUG] Improper tiling implementation in PULPOpen for Slice and ReduceMean operators #134

Open

diaconuccalin mentioned this pull request Nov 4, 2026

[BUG] Improper tiling implementation in PULPOpen for Slice and ReduceMean operators #134

Open

diaconuccalin requested changes Nov 4, 2026

View reviewed changes

diaconuccalin and others added 20 commits November 5, 2026 12:43

Remove leftover log file

668df06

Restored robust float handling in ConstantBuffer. Restored assert. Fi…

ece6ea9

…xed erronous binder type for Pad

Removed test models REAMDEs

1527ec6

Fixed naming of a test. Added the new tests to the CI pipeline

5e936bc

Update changelog

fa8be16

Fix Pad2D tiled.

f70def0

Pad fixes

e1d619d

Pad parser fix

29a83f2

Small BatchNorm fixes

cc4f985

mul_broadcast_fix

f6d6b65

ConvTranspose attributes fix

7c04a84

CI pipeline fixes

7fb9724

Fix formatting

c0a2cf8

Pad2D asymmetric width fix

321accf

Pad constraint cleanup

663151d

Fix docs CI pipeline

8778baf

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FrancescoConti commented Jul 30, 2026

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coderabbitai Bot commented Sep 22, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Summary by CodeRabbit

Sequence Diagram(s)

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Aldrago98 commented May 6, 2026 •

edited by diaconuccalin

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coderabbitai Bot commented Sep 22, 2026 •

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